code-practices

Code Practices Plugin for Claude Code

A collection of coding best practices for LLM-assisted development. These skills help you and your team write better code with Claude Code by applying proven patterns from Karpathy's insights and Anthropic's official best practices.

Installation

From GitHub

/plugin marketplace add rahulsub/code-practices-plugin

Local Development

/plugin marketplace add ~/code-practices-plugin

Skills Included

Development Workflow

Skill	Command	Description
Explore-Plan-Code	/explore-plan-code	Read files → plan → approve → implement workflow
Goal-Driven	/goal-driven	Define success criteria instead of step-by-step instructions
Test-First	/test-first	Write failing tests before implementation
Naive-Then-Optimize	/naive-then-optimize	Implement obvious solution first, then optimize

Code Quality

Skill	Command	Description
Code-Review	/code-review	Review code like a skeptical senior engineer
Simplify	/simplify	Remove over-engineering and unnecessary abstractions
Cleanup	/cleanup	Systematically remove dead code and tech debt
Surface-Assumptions	/surface-assumptions	Identify and validate hidden assumptions
Tradeoffs	/tradeoffs	Present multiple approaches with explicit tradeoffs

Advanced Workflows

Skill	Command	Description
Visual-Iteration	/visual-iteration	UI feedback loop with screenshots
Context-Management	/context-management	Manage long sessions with /clear, checklists, subagents
Multi-Claude	/multi-claude	Run parallel Claude instances for writer+reviewer patterns
Headless-Automation	/headless-automation	CI/CD integration, fan-out migrations, pipelines

Usage Examples

Before implementing a feature

/explore-plan-code

Claude will read relevant files, create a plan, and wait for your approval before coding.

After writing code

/code-review

Review the changes for hidden assumptions, over-engineering, and edge cases.

For complex algorithms

/naive-then-optimize

Implement the obvious correct solution first, then optimize while preserving behavior.

Attribution

These practices are based on:

• Karpathy's insights on LLM-assisted coding
• Anthropic's Claude Code Best Practices

License

MIT